Research on language recognition algorithm based on improved CFCC feature extraction

Hua LONG; Zhangheng HUANG; Yubin SHAO; Qingzhi DU; Shumeng SU

doi:10.11959/j.issn.1000-436x.2022234

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Research on language recognition algorithm based on improved CFCC feature extraction

Correspondences | 更新时间：2024-06-05

- Research on language recognition algorithm based on improved CFCC feature extraction
- Journal on Communications Vol. 43, Issue 12, Pages: 211-221(2022)
- 作者机构：
  
  昆明理工大学信息工程与自动化学院，云南昆明 650500
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61761025)
- DOI：10.11959/j.issn.1000-436x.2022234
  CLC： TN912.34
- Online First：2022-12，
  
  Published：25 December 2022
- 稿件说明：
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Hua LONG, Zhangheng HUANG, Yubin SHAO, et al. Research on language recognition algorithm based on improved CFCC feature extraction[J]. Journal on Communications, 2022, 43(12): 211-221.
DOI：

Hua LONG, Zhangheng HUANG, Yubin SHAO, et al. Research on language recognition algorithm based on improved CFCC feature extraction[J]. Journal on Communications, 2022, 43(12): 211-221. DOI： 10.11959/j.issn.1000-436x.2022234.

摘要

针对在低信噪比下语种识别准确率低的问题，提出一种基于分数阶小波变换的语种识别算法。首先，在特征提取前端采用自适应滤波法对带噪信号进行噪声滤除，以减小噪声对特征提取的影响，提升系统对带噪信号的处理能力。其次，采用新型分数阶小波变换作为小波基函数来模拟信号在耳蜗基底膜上的传播过程，利用非线性幂函数对信号进行压缩处理。最后，通过模拟人耳听觉过程提取改进耳蜗滤波器倒谱系数（CFCC）。实验结果表明，改进CFCC与传统CFCC相比显著提升了语种识别准确率，在0 dB信噪比下语种识别准确率平均提升了11.1%，充分验证了所提算法的有效性和稳健性。

Abstract

Aiming at the problem of low language recognition rate under low signal-to-noise ratio

a language recognition method based on fractional wavelet transform was proposed.Firstly

the adaptive filtering algorithm was used to filter the noise of the noisy signal

so as to reduce the influence of noise on the feature extraction and improve the processing ability of the system for non-stationary signals.Secondly

the motion of the signal on the basilar membrane of the cochlea was simulated

and then the signal was compressed by a nonlinear power function.Finally

the improved CFCC were extracted by simulating the human hearing process.Experiments show that compared with the traditional CFCC

the language recognition rate is significantly improved

and the language recognition rate is increased by 11.1% on average under the 0 dB signal-to-noise ratio

which verifies the effectiveness and robustness of the proposed algorithm.

关键词

Keywords

references

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